Although relatively rare, cardiomyopathy (CM) is among the leading important causes of morbidity and mortality in infants and children. Children with CM are commonly classified into four large functional groups: Dilated, Hypertrophic, and Restrictive, Arrhythmogenic Right Ventricular cardiomyopathy. The majority of cases are dilated cardiomyopathy (DCM). A substantial proportion die and these children make up the largest group of heart transplant recipients among children with CM. Therefore, they serve as an ideal group to identify predictors of outcomes, which include death and heart transplant. Predictors of outcomes have been described in various single institution studies, with many factors have been proposed including older age and decreased fractional shortening, among others, but none have been uniformly present in all studies. Only a few studies have employed multivariate Cox regression models. This study will capitalize on the pre-existing, robust dataset from the Pediatric Cardiomyopathy Registry (PCMR), which contains baseline clinical and demographic information, as well as extensive longitudinal follow-up. Supported by the NIH-NHLBI since 1995, the PCMR has enabled the pooling of data collected by centers across the US and Canada and permitted previously impossible analyses. Although prior PCMR analysis has included etiology of DCM in their model of prognostic factors, etiology-specific analyses based on baseline data, simulating real-time clinical practice, have yet to be performed. Semiparametric Kaplan- Meier and Cox Proportional Hazard methods for survival data analysis are commonly applied for independent outcomes, however, when multiple events of interest with possible dependence are under consideration, the parametric competing risk method of analysis may be more appropriate. In light of the challenges and gaps in knowledge presented, the following specific aims are proposed: AIM 1: Describe demographic and clinical factors measured at time of diagnosis. AIM 2: Identify factors measured at time of diagnosis which are prognostic of poor outcomes (death or trans- plant) utilizing the Cox proportional hazards regression method. AIM 3: Identify factors measured at time of diagnosis which are prognostic of poor outcomes (death or trans- plant) utilizing the competing risks method. Results of this study will greatly enhance the clinician's ability to interpret the risk of death, trans-plant, or long-term survival for a child with DCM. In turn, this will assist with the timely, efficient use of resources to improve the outcomes and quality of life for the child with this disease, his or her family, and society.